CN117347532B - HPLC characteristic spectrum detection method for Guizhi sugar-cellulitis granule preparation - Google Patents
HPLC characteristic spectrum detection method for Guizhi sugar-cellulitis granule preparation Download PDFInfo
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- 238000001228 spectrum Methods 0.000 title claims abstract description 39
- 238000001514 detection method Methods 0.000 title claims abstract description 36
- 238000004128 high performance liquid chromatography Methods 0.000 title claims abstract description 16
- 238000002360 preparation method Methods 0.000 title claims abstract description 12
- 206010007882 Cellulitis Diseases 0.000 title claims abstract description 9
- 239000008187 granular material Substances 0.000 title abstract description 9
- 239000013558 reference substance Substances 0.000 claims abstract description 30
- 239000002245 particle Substances 0.000 claims abstract description 29
- 239000000523 sample Substances 0.000 claims abstract description 28
- 238000000034 method Methods 0.000 claims abstract description 23
- 239000012488 sample solution Substances 0.000 claims abstract description 18
- OFOBLEOULBTSOW-UHFFFAOYSA-N Propanedioic acid Natural products OC(=O)CC(O)=O OFOBLEOULBTSOW-UHFFFAOYSA-N 0.000 claims abstract description 16
- VZCYOOQTPOCHFL-UPHRSURJSA-N maleic acid Chemical compound OC(=O)\C=C/C(O)=O VZCYOOQTPOCHFL-UPHRSURJSA-N 0.000 claims abstract description 16
- 239000011976 maleic acid Substances 0.000 claims abstract description 16
- VZCYOOQTPOCHFL-UHFFFAOYSA-N trans-butenedioic acid Natural products OC(=O)C=CC(O)=O VZCYOOQTPOCHFL-UHFFFAOYSA-N 0.000 claims abstract description 16
- DQNGMIQSXNGHOA-UMEXKXKESA-N (3e,6s,7s)-3-butylidene-6,7-dihydroxy-4,5,6,7-tetrahydro-2-benzofuran-1-one Chemical compound C1([C@@H]([C@@H](O)CC2)O)=C2C(=C/CCC)\OC1=O DQNGMIQSXNGHOA-UMEXKXKESA-N 0.000 claims abstract description 13
- VKJGBAJNNALVAV-UHFFFAOYSA-M Berberine chloride (TN) Chemical compound [Cl-].C1=C2CC[N+]3=CC4=C(OC)C(OC)=CC=C4C=C3C2=CC2=C1OCO2 VKJGBAJNNALVAV-UHFFFAOYSA-M 0.000 claims abstract description 12
- FBSKJMQYURKNSU-ZLSOWSIRSA-N acteoside Chemical compound O[C@@H]1[C@H](O)[C@@H](O)[C@H](C)O[C@H]1O[C@H]1[C@H](OC(=O)\C=C\C=2C=C(O)C(O)=CC=2)[C@@H](CO)O[C@@H](OCCC=2C=C(O)C(O)=CC=2)[C@@H]1O FBSKJMQYURKNSU-ZLSOWSIRSA-N 0.000 claims abstract description 12
- CWVRJTMFETXNAD-JUHZACGLSA-N chlorogenic acid Chemical compound O[C@@H]1[C@H](O)C[C@@](O)(C(O)=O)C[C@H]1OC(=O)\C=C\C1=CC=C(O)C(O)=C1 CWVRJTMFETXNAD-JUHZACGLSA-N 0.000 claims abstract description 12
- CWVRJTMFETXNAD-FWCWNIRPSA-N 3-O-Caffeoylquinic acid Natural products O[C@H]1[C@@H](O)C[C@@](O)(C(O)=O)C[C@H]1OC(=O)\C=C\C1=CC=C(O)C(O)=C1 CWVRJTMFETXNAD-FWCWNIRPSA-N 0.000 claims abstract description 11
- PZIRUHCJZBGLDY-UHFFFAOYSA-N Caffeoylquinic acid Natural products CC(CCC(=O)C(C)C1C(=O)CC2C3CC(O)C4CC(O)CCC4(C)C3CCC12C)C(=O)O PZIRUHCJZBGLDY-UHFFFAOYSA-N 0.000 claims abstract description 11
- CWVRJTMFETXNAD-KLZCAUPSSA-N Neochlorogenin-saeure Natural products O[C@H]1C[C@@](O)(C[C@@H](OC(=O)C=Cc2ccc(O)c(O)c2)[C@@H]1O)C(=O)O CWVRJTMFETXNAD-KLZCAUPSSA-N 0.000 claims abstract description 11
- 229930185474 acteoside Natural products 0.000 claims abstract description 11
- FBSKJMQYURKNSU-UKQWSTALSA-N acteoside I Natural products C[C@@H]1O[C@H](O[C@@H]2[C@@H](O)[C@H](OCCc3ccc(O)c(O)c3)O[C@H](CO)[C@H]2OC(=O)C=Cc4ccc(O)c(O)c4)[C@H](O)[C@H](O)[C@H]1O FBSKJMQYURKNSU-UKQWSTALSA-N 0.000 claims abstract description 11
- 229940074393 chlorogenic acid Drugs 0.000 claims abstract description 11
- FFQSDFBBSXGVKF-KHSQJDLVSA-N chlorogenic acid Natural products O[C@@H]1C[C@](O)(C[C@@H](CC(=O)C=Cc2ccc(O)c(O)c2)[C@@H]1O)C(=O)O FFQSDFBBSXGVKF-KHSQJDLVSA-N 0.000 claims abstract description 11
- 235000001368 chlorogenic acid Nutrition 0.000 claims abstract description 11
- BMRSEYFENKXDIS-KLZCAUPSSA-N cis-3-O-p-coumaroylquinic acid Natural products O[C@H]1C[C@@](O)(C[C@@H](OC(=O)C=Cc2ccc(O)cc2)[C@@H]1O)C(=O)O BMRSEYFENKXDIS-KLZCAUPSSA-N 0.000 claims abstract description 11
- 239000002904 solvent Substances 0.000 claims abstract description 11
- QFRYQWYZSQDFOS-UHFFFAOYSA-N verbascoside Natural products CC1OC(COC2C(O)C(COC3OC(C(O)C(O)C3O)C(=O)O)OC(Oc4cc(O)cc5OC(=CC(=O)c45)c6ccc(O)c(O)c6)C2O)C(O)C(O)C1O QFRYQWYZSQDFOS-UHFFFAOYSA-N 0.000 claims abstract description 11
- DQNGMIQSXNGHOA-UHFFFAOYSA-N senkyunolide-H Natural products C1CC(O)C(O)C2=C1C(=CCCC)OC2=O DQNGMIQSXNGHOA-UHFFFAOYSA-N 0.000 claims abstract description 10
- VUWOVGXVRYBSGI-IRXABLMPSA-N 1,3,6-trihydroxy-2-[(2s,3r,4r,5s,6r)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]-7-[(2s,3r,4r,5s,6r)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxyxanthen-9-one Chemical compound O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1OC1=CC(C(C2=C(O)C([C@H]3[C@@H]([C@@H](O)[C@H](O)[C@@H](CO)O3)O)=C(O)C=C2O2)=O)=C2C=C1O VUWOVGXVRYBSGI-IRXABLMPSA-N 0.000 claims abstract description 9
- 239000000706 filtrate Substances 0.000 claims abstract description 9
- VUWOVGXVRYBSGI-UHFFFAOYSA-N 7-O-beta-D-Glucopyranoside-Mangiferin Natural products OC1C(O)C(O)C(CO)OC1OC1=CC(C(C2=C(O)C(C3C(C(O)C(O)C(CO)O3)O)=C(O)C=C2O2)=O)=C2C=C1O VUWOVGXVRYBSGI-UHFFFAOYSA-N 0.000 claims abstract description 8
- WAYBWHJDPRHBMW-GUZANKGOSA-N neomangiferin Natural products OC[C@@H]1O[C@@H]([C@@H](O)[C@H](O)[C@H]1O)c2cc3C(=O)c4c(O)c([C@@H]5O[C@H](CO)[C@@H](O)[C@H](O)[C@H]5O)c(O)cc4Oc3cc2O WAYBWHJDPRHBMW-GUZANKGOSA-N 0.000 claims abstract description 8
- 229920002160 Celluloid Polymers 0.000 claims abstract description 6
- 238000001816 cooling Methods 0.000 claims abstract description 6
- 238000001914 filtration Methods 0.000 claims abstract description 6
- 238000010828 elution Methods 0.000 claims abstract description 5
- 229940098895 maleic acid Drugs 0.000 claims abstract description 4
- 238000002137 ultrasound extraction Methods 0.000 claims abstract description 4
- 230000014759 maintenance of location Effects 0.000 claims description 41
- 208000035484 Cellulite Diseases 0.000 claims description 22
- 206010049752 Peau d'orange Diseases 0.000 claims description 22
- 239000000243 solution Substances 0.000 claims description 20
- WEVYAHXRMPXWCK-UHFFFAOYSA-N Acetonitrile Chemical compound CC#N WEVYAHXRMPXWCK-UHFFFAOYSA-N 0.000 claims description 12
- OKKJLVBELUTLKV-UHFFFAOYSA-N Methanol Chemical compound OC OKKJLVBELUTLKV-UHFFFAOYSA-N 0.000 claims description 12
- NBIIXXVUZAFLBC-UHFFFAOYSA-N Phosphoric acid Chemical group OP(O)(O)=O NBIIXXVUZAFLBC-UHFFFAOYSA-N 0.000 claims description 8
- 230000036232 cellulite Effects 0.000 claims description 8
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 6
- 238000002347 injection Methods 0.000 claims description 5
- 239000007924 injection Substances 0.000 claims description 5
- 229910000147 aluminium phosphate Inorganic materials 0.000 claims description 4
- 239000007864 aqueous solution Substances 0.000 claims description 3
- 239000000945 filler Substances 0.000 claims description 2
- YTJSFYQNRXLOIC-UHFFFAOYSA-N octadecylsilane Chemical compound CCCCCCCCCCCCCCCCCC[SiH3] YTJSFYQNRXLOIC-UHFFFAOYSA-N 0.000 claims description 2
- 239000000377 silicon dioxide Substances 0.000 claims description 2
- 238000002604 ultrasonography Methods 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 9
- 239000000126 substance Substances 0.000 abstract description 9
- 238000004090 dissolution Methods 0.000 abstract description 2
- 239000012535 impurity Substances 0.000 abstract description 2
- 229940126680 traditional chinese medicines Drugs 0.000 abstract description 2
- 239000000825 pharmaceutical preparation Substances 0.000 description 26
- 239000003814 drug Substances 0.000 description 11
- DQNGMIQSXNGHOA-JXQVETIVSA-N (3z,6s,7s)-3-butylidene-6,7-dihydroxy-4,5,6,7-tetrahydro-2-benzofuran-1-one Chemical compound C1([C@@H]([C@@H](O)CC2)O)=C2C(=C/CCC)/OC1=O DQNGMIQSXNGHOA-JXQVETIVSA-N 0.000 description 8
- 238000002474 experimental method Methods 0.000 description 8
- 229940079593 drug Drugs 0.000 description 7
- 238000007689 inspection Methods 0.000 description 6
- AEDDIBAIWPIIBD-ZJKJAXBQSA-N mangiferin Chemical compound O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1C1=C(O)C=C(OC=2C(=CC(O)=C(O)C=2)C2=O)C2=C1O AEDDIBAIWPIIBD-ZJKJAXBQSA-N 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 5
- 235000013305 food Nutrition 0.000 description 5
- 239000007788 liquid Substances 0.000 description 4
- 239000012528 membrane Substances 0.000 description 4
- 239000006228 supernatant Substances 0.000 description 4
- 238000009210 therapy by ultrasound Methods 0.000 description 4
- 206010017711 Gangrene Diseases 0.000 description 3
- YWQSXCGKJDUYTL-UHFFFAOYSA-N Mangiferin Natural products CC(CCC=C(C)C)C1CC(C)C2C3CCC4C(C)(C)CCCC45CC35CCC12C YWQSXCGKJDUYTL-UHFFFAOYSA-N 0.000 description 3
- 238000012937 correction Methods 0.000 description 3
- 229940043357 mangiferin Drugs 0.000 description 3
- 238000003908 quality control method Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 241000205585 Aquilegia canadensis Species 0.000 description 2
- 208000008960 Diabetic foot Diseases 0.000 description 2
- ZPIKVDODKLJKIN-NSHDSACASA-N Senkyunolide Chemical compound C1CC=CC2=C1[C@H](CCCC)OC2=O ZPIKVDODKLJKIN-NSHDSACASA-N 0.000 description 2
- 239000004480 active ingredient Substances 0.000 description 2
- 239000003153 chemical reaction reagent Substances 0.000 description 2
- 238000012417 linear regression Methods 0.000 description 2
- GBMDVOWEEQVZKZ-UHFFFAOYSA-N methanol;hydrate Chemical compound O.OC GBMDVOWEEQVZKZ-UHFFFAOYSA-N 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- 238000013112 stability test Methods 0.000 description 2
- 239000003643 water by type Substances 0.000 description 2
- 241000382455 Angelica sinensis Species 0.000 description 1
- 206010007247 Carbuncle Diseases 0.000 description 1
- 241000037740 Coptis chinensis Species 0.000 description 1
- 244000303040 Glycyrrhiza glabra Species 0.000 description 1
- 235000006200 Glycyrrhiza glabra Nutrition 0.000 description 1
- 241000112528 Ligusticum striatum Species 0.000 description 1
- 241000405414 Rehmannia Species 0.000 description 1
- 238000001647 drug administration Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- LPLVUJXQOOQHMX-QWBHMCJMSA-N glycyrrhizinic acid Chemical compound O([C@@H]1[C@@H](O)[C@H](O)[C@H](O[C@@H]1O[C@@H]1C([C@H]2[C@]([C@@H]3[C@@]([C@@]4(CC[C@@]5(C)CC[C@@](C)(C[C@H]5C4=CC3=O)C(O)=O)C)(C)CC2)(C)CC1)(C)C)C(O)=O)[C@@H]1O[C@H](C(O)=O)[C@@H](O)[C@H](O)[C@H]1O LPLVUJXQOOQHMX-QWBHMCJMSA-N 0.000 description 1
- 125000005842 heteroatom Chemical group 0.000 description 1
- 229940042040 innovative drug Drugs 0.000 description 1
- 235000011477 liquorice Nutrition 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 229930183325 senkyunolide Natural products 0.000 description 1
- 239000012086 standard solution Substances 0.000 description 1
- 239000012085 test solution Substances 0.000 description 1
- 229910021642 ultra pure water Inorganic materials 0.000 description 1
- 239000012498 ultrapure water Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/26—Conditioning of the fluid carrier; Flow patterns
- G01N30/28—Control of physical parameters of the fluid carrier
- G01N30/34—Control of physical parameters of the fluid carrier of fluid composition, e.g. gradient
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8624—Detection of slopes or peaks; baseline correction
- G01N30/8631—Peaks
- G01N30/8634—Peak quality criteria
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
- G01N30/8679—Target compound analysis, i.e. whereby a limited number of peaks is analysed
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8675—Evaluation, i.e. decoding of the signal into analytical information
- G01N30/8686—Fingerprinting, e.g. without prior knowledge of the sample components
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
- Y02A50/30—Against vector-borne diseases, e.g. mosquito-borne, fly-borne, tick-borne or waterborne diseases whose impact is exacerbated by climate change
Abstract
The invention belongs to the technical field of analysis of effective components of traditional Chinese medicines, and relates to a detection method of an HPLC characteristic spectrum of a Guizhi sugar-cellulitis granule preparation. Adding a known cellulitis particle sample into a solvent for dissolution, performing ultrasonic extraction, cooling, shaking uniformly, filtering, and taking a sample solution obtained by subsequent filtrate; dissolving the reference substance with the same solvent to obtain a reference substance solution; and respectively measuring the sample solution and the reference substance solution by adopting a high performance liquid chromatography to obtain characteristic patterns, comparing the characteristic patterns of the sample with the characteristic patterns of the reference substance, and carrying out attribution positioning on the components in the sample solution so as to obtain the characteristic patterns of the known celluloid particles. The detection method provided by the invention adopts a gradient elution method, and solves the problems that a plurality of characteristic peaks are difficult to separate and the interference of impurity peaks is difficult. Simple operation and high precision, and can detect six substances of senkyunolide I, neomangiferin, chlorogenic acid, maleic acid, berberine hydrochloride and acteoside at one time.
Description
Technical Field
The invention belongs to the technical field of analysis of effective components of traditional Chinese medicines, and relates to a detection method of an HPLC characteristic spectrum of a Guizhi sugar-cellulitis granule preparation.
Background
The cuisine carbuncle particle is the first Chinese medicine innovative drug approved by the national drug administration to enter clinic and is the only one diabetic foot, and the registration number of the drug clinical trial registration and information platform is CTR 20201182. The preparation method comprises the following steps: the preparation method comprises the steps of taking angelica sinensis, rhizoma anemarrhenae, honeysuckle stem, honeysuckle, coptis chinensis, rehmannia root, radix scrophulariae, liquorice, fructus polygoni orientalis, ligusticum wallichii and radix ophiopogonis as raw materials, adopting an extraction and concentration unit to prepare the known cellulite extract, adding a certain amount of auxiliary materials to prepare particles, namely the known cellulite particles, and the known cellulite particles have important significance in treating diabetic feet.
Because of the lack of an overall evaluation method for each component in the pharmaceutical preparation, no effective quality evaluation method exists in the preparation at present, and therefore, effective quality control of the effective components of the pharmaceutical preparation cannot be performed at present. In addition, the pharmaceutical preparation contains a large amount of active ingredients, and if pretreatment or chromatographic detection conditions are not ideal, the obtained chromatogram has a large number of hetero peaks, and the overlapping or tailing of each chromatographic peak is serious, so that analysis cannot be performed.
Disclosure of Invention
The invention provides a novel HPLC (high performance liquid chromatography) characteristic spectrum detection method of a known sugar-cellulite granule preparation aiming at the problems existing in the detection of the effective components of the traditional known sugar-cellulite granules.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
the HPLC characteristic spectrum detection method of the granule preparation of the known sugar gangrene comprises the steps of adding a sample of the known sugar gangrene granule into a solvent for dissolution, then carrying out ultrasonic extraction, cooling, shaking, filtering, and taking a sample solution obtained by the subsequent filtrate; dissolving the reference substance with the same solvent to obtain a reference substance solution; respectively measuring the sample solution and the reference substance solution by adopting a high performance liquid chromatography to obtain characteristic patterns of the sample solution and the reference substance solution, comparing the retention time of the characteristic patterns of the sample solution with the characteristic patterns of the reference substance solution for qualitative determination, and carrying out attribution positioning on index components in the characteristic patterns of the sample solution so as to obtain the characteristic patterns of the known celluloid particles; the detection conditions were as follows.
C18 chromatographic column, model: ZORBAX Eclipse XDB-C18, specification: 4.6X250 mm,5 μm, octadecylsilane chemically bonded silica as filler in chromatographic column, mobile phase including A phase and B phase, wherein A phase is acetonitrile, B phase is phosphoric acid water solution with mass fraction of 0.2%, detection wavelength is 298nm, flow rate is 0.7-0.8mL/min, column temperature is 34 ℃, and sample injection amount is 10 μl.
The specific procedure of gradient elution is:
0-1min, phase A: the volume ratio of the phase B is 6:94;
1-3min, phase A: the volume ratio of the phase B is 6:94 becomes 18:82;
3-15min, phase A: the volume ratio of the phase B is 18:82 becomes 25:75;
15-27min, phase A: the volume ratio of the phase B is 25:75 to 46:54;
27-43min, phase A: the volume ratio of phase B is 46:54 becomes 65:35;
43-55min, phase A: the volume ratio of the phase B is 65:35 becomes 6:94.
the invention provides application of the detection method in simultaneous detection of 6 substances including senkyunolide, neomangiferin, chlorogenic acid, maleic acid, berberine hydrochloride and acteoside which are effective components of the known cellulite granular preparation.
The invention provides a detection method of HPLC characteristic spectrum of a known sugar-gangrene granule pharmaceutical preparation, after a sample is pretreated, the HPLC characteristic spectrum of the pharmaceutical preparation is established by adopting high performance liquid chromatography for detection, 6 chemical components in the pharmaceutical preparation can be simultaneously analyzed at one time, the detection method is used for solving the problem of lacking the whole evaluation method of the pharmaceutical preparation in the prior art, and the quality control system of the pharmaceutical preparation is comprehensively perfected.
Preferably, the volume fraction of methanol in the solvent methanol aqueous solution is 50%, the concentration of the celluloid particles sample in the solvent is 0.02-0.07g/mL, and the ultrasonic time is 30-60min.
Preferably, the pharmaceutical preparation is dissolved in a solvent, and then subjected to ultrasonic extraction, cooling, shaking, filtering with a filter membrane, discarding the primary filtrate, and taking a sample solution obtained from the subsequent filtrate.
Preferably, the filtering mode is to take supernatant of the solution after shaking, and filter the supernatant with a filter membrane pore size of 0.22 μm.
The HPLC characteristic spectrum detection method of the known sugar-cellulite particles is adopted to detect a plurality of known sugar-cellulite particle samples respectively, the characteristic spectrum of the known sugar-cellulite particles is obtained to generate a common contrast characteristic spectrum, chromatographic peaks existing in the spectrum are used as common characteristic peaks, the relative retention time of the common characteristic peaks is determined, and the standard characteristic spectrum of the known sugar-cellulite particles is established. The standard characteristic spectrum of the obtained known celluloid particles comprises 7 common characteristic peaks, a 5-numbered peak is taken as a reference peak S peak, the relative retention time is 1.000, the relative retention time of other 6 common characteristic peaks is sequentially a 1-numbered peak with the relative retention time of 0.125, a 2-numbered peak with the relative retention time of 0.371, a 3-numbered peak with the relative retention time of 0.710, a 4-numbered peak with the relative retention time of 0.962, a 6-numbered peak with the relative retention time of 1.011 and a 7-numbered peak with the relative retention time of 1.064, and the relative deviation of the relative retention time of the 6 common characteristic peaks except the 5-numbered peak is less than or equal to +/-5 percent. Comparing the standard characteristic spectrum of the known cellulitis particles with the characteristic spectrum of the reference substance solution, and positioning and determining that the peak 1 is the characteristic peak of senkyunolide I, wherein the CAS number of senkyunolide I is 88551-87-5; the peak No. 2 is a characteristic peak of the new mangiferin, and the CAS number of the new mangiferin is 64809-67-2; the peak No. 3 is a characteristic peak of chlorogenic acid, and the CAS number of chlorogenic acid is 327-97-9; the peak No. 5 is a characteristic peak of the maleic acid, and the CAS number of the maleic acid is 22255-40-9; the No. 6 peak is a characteristic peak of berberine hydrochloride, and the CAS number of the berberine hydrochloride is 633-65-8; the peak No. 7 is a characteristic peak of the acteoside; the acteoside has CAS number 61276-17-3.
Compared with the prior art, the invention has the advantages and positive effects that:
1. the detection method provided by the invention adopts a gradient elution method, and solves the problems that a plurality of characteristic peaks are difficult to separate and the interference of impurity peaks is difficult. The characteristic components of the known sugar-cellulite particles can be separated and detected, the component information of the known sugar-cellulite particles can be comprehensively reflected, and the applications of quality control of the characteristic spectrum of the known sugar-cellulite particles, monitoring of the production of the known sugar-cellulite particles, identification of authenticity of the known sugar-cellulite particles and the like are realized.
2. The detection method provided by the invention has the advantages of simple operation, high precision, good stability and reproducibility and the like, and can detect six substances of senkyunolide I, neomangiferin, chlorogenic acid, maleic acid, berberine hydrochloride and acteoside of the known cellulite particles at one time.
Drawings
FIG. 1 shows the results of the precision experiments generated by the average method.
FIG. 2 shows the results of stability experiments generated by the average method.
FIG. 3 shows the results of the repeatability experiments generated by the average method.
FIG. 4 is a graph showing the detection patterns of the control and pharmaceutical preparation in example 1.
Detailed Description
In order that the above objects, features and advantages of the invention will be more clearly understood, a further description of the invention will be provided with reference to specific examples. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as described herein, and therefore the present invention is not limited to the specific embodiments of the disclosure that follow.
Example 1
1. Reagent(s)
In this example, 15 batches of known cellulite particles were tested, and different batches of known cellulite particles were provided by Tangning pharmaceutical technologies (Jinan) Inc. The numbers of the 15 batches of the known cellulitis particles are 20220301, 20220302, 20220303, 20220401, 20220402, 20220403, 20220501, 20220502, 20220503, 20220601, 20220602, 20220603, 20220701, 20220702 and 20220703 in sequence.
The control is as follows: senkyunolide I control (China food and drug inspection institute, 112071-202101), neomangiferin control (Shanghai Seiyuan leaf Biotechnology Co., ltd., B21397), chlorogenic acid control (China food and drug inspection institute, 110753-202119), maleic acid control (China food and drug inspection institute, 111865-202005), berberine hydrochloride control (China food and drug inspection institute, 110713-202316), acteoside control (China food and drug inspection institute, 111530-201914).
Methanol, acetonitrile (chromatographic purity, merck, batch number: L1099407026, 224399); phosphoric acid (analytical purity, national drug group chemical agent Co., ltd., lot number: 10015410); ultrapure water (prepared by a pure water meter).
2. Instrument for measuring and controlling the intensity of light
The main instruments of this embodiment are as follows: waters ACQUITY high performance liquid chromatograph (Waters, U.S. quaternary pump), column: ZORBAX Eclipse XDB-C18 chromatographic column, specification: 4.6X250 mm,5 μm; ZP15D1 type ultrasonic pure water apparatus (Shanghai right instruments, limited, china).
3. Sample processing and detection
Taking 1g of various batches of known celluloid particles, respectively dissolving in 50ml of 50% methanol water solution, respectively carrying out ultrasonic treatment for 30min, cooling the solution to room temperature after ultrasonic treatment, shaking uniformly, taking supernatant, passing through a 0.22 filter membrane, removing primary filtrate, and taking subsequent filtrate to obtain a sample solution.
Accurately weighing senkyunolide I reference substance, new mangiferin reference substance, chlorogenic acid reference substance, maleic acid reference substance, berberine hydrochloride reference substance and acteoside reference substance 1g, respectively dissolving in 50ml of 50% methanol water solution with volume fraction, respectively performing ultrasonic treatment for 30min, cooling the solution to room temperature after ultrasonic treatment, shaking, collecting supernatant, filtering with 0.22 filter membrane, removing primary filtrate, and collecting subsequent filtrate to obtain each component reference substance solution.
Sufficient reagents of phase A and phase B are prepared for use, wherein phase A is acetonitrile and phase B is 0.2% phosphoric acid aqueous solution by mass fraction. The detector of the liquid chromatograph is a Diode Array Detector (DAD), the chromatographic column is a C18 chromatographic column, the detection wavelength of the liquid chromatograph is 298nm, the flow rate is 0.75mL/min, the column temperature is 34 ℃, and the detection is carried out according to the following procedure, wherein the sample introduction amounts of the sample to be detected and the reference substance are 10 mu L.
The specific procedure of gradient elution is:
0-1min, phase A: the volume ratio of the phase B is 6:94;1-3min, phase A: the volume ratio of the phase B is 6:94 becomes 18:82;3-15min, phase A: the volume ratio of the phase B is 18:82 becomes 25:75;15-27min, phase A: the volume ratio of the phase B is 25:75 to 46:54;27-43min, phase A: the volume ratio of phase B is 46:54 becomes 65:35;43-55min, phase A: the volume ratio of the phase B is 65:35 becomes 6:94.
15 batches of pharmaceutical preparations are sequentially numbered A1-A15, 15 batches of pharmaceutical preparations and reference substances are sequentially detected to obtain a map of each pharmaceutical preparation, then each reference substance is sampled and detected under the same condition after being uniformly mixed with 1ml of each reference substance to generate a common reference characteristic map, chromatographic peaks existing in the map are used as common characteristic peaks, the relative retention time of the common characteristic peaks is determined, and then the relative retention time of the common characteristic peaks is compared with the retention time of the spectrograms of the reference substances to obtain a standard characteristic map of the pharmaceutical preparation, as shown in fig. 4, the upper part of the figure 4 is a reference substance mixed liquid detection map, and the lower part is a pharmaceutical preparation sample detection map numbered A1, and as can be seen from fig. 4, each substance peak type is better, tailing, overlapping and other conditions are avoided, so that the chromatographic detection conditions provided by the invention are reasonable and feasible.
The standard characteristic spectrum of the pharmaceutical preparation finally obtained through repeated detection comprises 7 common characteristic peaks, a 5-numbered peak is taken as a reference peak S peak, the relative retention time is 1.000, the relative retention time of other 6 common characteristic peaks is defined as a 1-numbered peak with the relative retention time of 0.125, a 2-numbered peak with the relative retention time of 0.371, a 3-numbered peak with the relative retention time of 0.710, a 4-numbered peak with the relative retention time of 0.962, a 6-numbered peak with the relative retention time of 1.011 and a 7-numbered peak with the relative retention time of 1.064, and the relative deviation of the relative retention time of the 6 common characteristic peaks except the 5-numbered peak is less than or equal to 5%.
The standard characteristic spectrum of the pharmaceutical preparation is compared with the characteristic spectrum of the reference substance solution, the characteristic peak of senkyunolide I is positioned and determined, the characteristic peak of neomangiferin is identified as peak No. 1, chlorogenic acid is identified as peak No. 3, maleic acid is identified as peak No. 5, berberine hydrochloride is identified as peak No. 6, and acteoside is identified as peak No. 7.
The relative retention time of each characteristic peak was calculated using the maleic acid as a reference peak, and the results are shown in table 1. As can be seen from Table 1, the relative retention time of each characteristic peak in the characteristic spectrum of 15 batches of the pharmaceutical preparation samples was 0-2.0%. And the relative deviation of the specified values of the relative retention time of other 6 peaks is less than or equal to +/-5% by taking a maleic acid peak as a reference, wherein the specified values are as follows: 0.125 (senkyunolide I), 0.371 (neomangiferin), 0.710 (chlorogenic acid), 0.962 (maleic acid), 1.011 (berberine hydrochloride), and 1.064 (acteoside).
TABLE 1 measurement results for 15 samples
The detection method provided by the invention is verified, and the verification process and the result are as follows.
(1) Precision inspection
Sample A1 (lot number 20220301) of the same pharmaceutical preparation was taken, a sample solution was prepared and obtained according to the sample treatment procedure in example 1, the sample was continuously introduced and analyzed 6 times a day according to the chromatographic conditions in example 1, each sample was recorded as S1-S6, the relative retention time of each peak was calculated with respect to the reference peak of the maleic acid, and the results are shown in Table 2. As can be seen from Table 2, the relative retention time of each characteristic peak was less than 2% RSD.
TABLE 2 results of precision experiments
The obtained characteristic spectrum is imported into a traditional Chinese medicine chromatographic fingerprint similarity evaluation system (2012 edition), the characteristic spectrum of the 1 st analysis result S1 of the pharmaceutical preparation sample A1 is set as a reference spectrum, the time window width is set to be 0.1, multipoint correction is carried out, mark peak matching is carried out, a comparison characteristic spectrum R is generated by an average method, and the specific result is shown in figure 1. In fig. 1, S1-S6 are feature spectrograms obtained by 6 sample injections, R represents a comparison feature spectrogram, wherein S1 is an original spectrogram, S2-S6 and the comparison feature spectrogram are obtained by translating the whole spectrogram vertically upwards on the basis of the original spectrogram without changing the peak position on the abscissa. The similarity was calculated for the results of the precision experiments, and the results are shown in Table 2. As shown in FIG. 1 and Table 3, the similarity of the 6 sample injection results is not less than 0.999, which indicates that the method has good precision.
TABLE 3 precision similarity results
(2) Stability of
Sample A1 (batch No. 20220301) of the same pharmaceutical preparation was taken, a sample solution was prepared according to the procedure of example 1, sample analysis was performed according to the chromatographic conditions of example 1, which were different from those of sample analysis performed at 0h, 4h, 8h, 12h and 24h, and relative retention time of each characteristic peak was calculated by using maleic acid as a reference peak, and specific stability results are shown in Table 4. The results show that the RSD relative to the retention time is less than 2%.
TABLE 4 stability test results
The obtained characteristic spectrum is imported into a traditional Chinese medicine chromatographic fingerprint similarity evaluation system (2012 edition), the characteristic spectrum of an analysis result S1 of the pharmaceutical preparation sample A1 at 0h is set as a reference spectrum, the time window width is set as 0.1, multipoint correction is carried out, mark peak matching is carried out, a comparison characteristic spectrum R is generated by an average method, and a specific result is shown in figure 2. The spectrogram shown in 0h in fig. 2 is obtained from actual detection data, and the rest spectrograms are obtained by vertically and upwardly translating the peak-emitting signal curve on the premise of keeping the peak-emitting time unchanged. Similarity was calculated for the stability test results, which are shown in table 5. As can be seen from the results of FIG. 2 and Table 5, the similarity of the feature patterns at 5 time points is not less than 0.999, which indicates that the sample is stable within 24 hours, and the method has good stability.
TABLE 5 stability similarity results
(3) Repeatability of
The same pharmaceutical preparation sample A1 (lot number 20220301) was prepared in parallel to obtain 6 test solutions, designated S1-S6, respectively, and the control group designated R, according to the procedure of example 1. Analysis was performed by separate sample injection under the chromatographic conditions of example 1, with maleic acid as the reference peak, and the relative retention time of each peak was calculated, with specific results of reproducibility as shown in Table 6. The results show that the relative retention time of each characteristic peak has an RSD of less than 2%.
TABLE 6 results of repeatability experiments
The obtained characteristic spectrum is imported into a traditional Chinese medicine chromatographic fingerprint similarity evaluation system (2012 edition), the characteristic spectrum of the 1 st sample solution analysis result S1 of the pharmaceutical preparation sample A1 is set as a reference spectrum, the time window width is set as 0.1, multipoint correction is carried out, mark peak matching is carried out, a comparison characteristic spectrum R is generated by an average method, and the specific result is shown in figure 3. In fig. 3, the S1 curve spectrum is actual detection data, and the rest spectra are obtained by vertically and upwardly translating the peak signal curve on the premise of keeping the peak time unchanged. Similarity was calculated for the results of the repeatability experiments, and the results are shown in table 7. As can be seen from FIG. 3 and Table 7, the similarity of the characteristic patterns of the 6 samples is 1, which indicates that the method has good repeatability.
TABLE 7 repeatability similarity results
According to the chromatographic detection conditions provided by the invention, the content of each substance in the pharmaceutical preparation can be judged by utilizing the peak area under the same treatment conditions so as to preliminarily judge the content of the active ingredients of the pharmaceutical preparation, or on the basis of the detection method provided by the invention, standard solutions with different concentrations of each substance can be further prepared for linear regression, a linear regression equation is obtained, and the labeled recovery rate is calculated through a labeled recovery experiment so as to further quantitatively determine each substance.
The present invention is not limited to the above-mentioned embodiments, and any equivalent embodiments which can be changed or modified by the technical content disclosed above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above-mentioned embodiments according to the technical substance of the present invention without departing from the technical content of the present invention still belong to the protection scope of the technical solution of the present invention.
Claims (2)
1. A method for simultaneously detecting 6 effective components in a known sugar-cellulite granular preparation is characterized by adopting an HPLC characteristic spectrum for detection, wherein the 6 effective components in the known sugar-cellulite granular preparation are senkyunolide I, neomangiferin, chlorogenic acid, maleic acid, berberine hydrochloride and acteoside, and the detection process is as follows:
adding a solvent into a known cellulitis particle sample for dissolving, performing ultrasonic extraction, cooling, shaking uniformly, filtering, and taking a subsequent filtrate to obtain a sample solution; dissolving the reference substance with the same solvent to obtain a reference substance solution; respectively measuring the sample solution and the reference substance solution by adopting a high performance liquid chromatography to obtain characteristic patterns of the sample solution and the reference substance solution, comparing the characteristic patterns of the sample solution with the characteristic patterns of the reference substance solution to determine the retention time, and performing attribution positioning on index components in the characteristic patterns of the sample solution so as to obtain the characteristic patterns of the known celluloid particles; the high performance liquid chromatography detection conditions are as follows:
a C18 chromatographic column, wherein the filler in the chromatographic column is octadecylsilane chemically bonded silica, the detection wavelength is 298nm, the mobile phase comprises a phase A and a phase B, wherein the phase A is acetonitrile, the phase B is phosphoric acid aqueous solution with the mass fraction of 0.2%, the flow rate is 0.7-0.8mL/min, the column temperature is 34 ℃, the sample injection amount is 10 mu L,
the specific procedure of gradient elution is:
0-1min, phase A: the volume ratio of the phase B is 6:94;
1-3min, phase A: the volume ratio of the phase B is 6:94 to 18:82;
3-15min, phase A: the volume ratio of the phase B is 18:82 becomes 25:75;
15-27min, phase A: the volume ratio of the phase B is 25:75 to 46:54;
27-43min, phase A: the volume ratio of phase B is 46:54 becomes 65:35;
43-55min, phase A: the volume ratio of the phase B is 65:35 becomes 6:94;
the standard characteristic spectrum of the obtained known cellulitis particles comprises 7 common characteristic peaks, the relative retention time is 1.000 by taking a No. 5 peak as a reference peak S peak, and the relative retention time of other 6 common characteristic peaks is defined by the following specified values in sequence: peak relative retention time No. 1 is 0.125, peak relative retention time No. 2 is 0.371, peak relative retention time No. 3 is 0.710, peak relative retention time No. 4 is 0.962, peak relative retention time No. 6 is 1.011, peak relative retention time No. 7 is 1.064; comparing standard characteristic spectrum of the known sugar-cellulite particles with characteristic spectrum of reference substance solution, positioning and determining that the No. 1 peak is characteristic peak of senkyunolide I, the No. 2 peak is characteristic peak of neomangiferin, the No. 3 peak is characteristic peak of chlorogenic acid, the No. 5 peak is characteristic peak of maleic acid, the No. 6 peak is characteristic peak of berberine hydrochloride, and the No. 7 peak is characteristic peak of acteoside.
2. The method according to claim 1, wherein the solvent is a 50% methanol solution, the concentration of the known cellulite particle sample in the solvent is 0.02-0.07g/mL, and the ultrasound time is 30-60min.
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